Danjun Li

Integrated drought risk assessment of multi-hazard-affected bodies based on copulas in the Taoerhe Basin, China

Along with global warming, drought disasters are occurring more frequently and are seriously affecting normal life and food security in China. Drought risk assessments are necessary to provide support for local governments. This study aimed to establish an integrated drought risk model based on the relation curve of drought joint probabilities and drought losses of multi-hazard-affected bodies. First, drought characteristics, including duration and severity, were classified using the 1953–2010 precipitation anomaly in the Taoerhe Basin based on run theory, and their marginal distributions were identified by exponential and Gamma distributions, respectively. Then, drought duration and severity were related to construct a joint probability distribution based on the copula function. We used the EPIC (Environmental Policy Integrated Climate) model to simulate maize yield and historical data to calculate the loss rates of agriculture, industry, and animal husbandry in the study area. Next, we constructed vulnerability curves. Finally, the spatial distributions of drought risk for 10-, 20-, and 50-year return periods were expressed using inverse distance weighting. Our results indicate that the spatial distributions of the three return periods are consistent. The highest drought risk is in Ulanhot, and the duration and severity there were both highest. This means that higher drought risk corresponds to longer drought duration and larger drought severity, thus providing useful information for drought and water resource management. For 10-, 20-, and 50-year return periods, the drought risk values ranged from 0.41 to 0.53, 0.45 to 0.59, and 0.50 to 0.67, respectively. Therefore, when the return period increases, the drought risk increases.

Maize Drought and Flood Disaster Hazard Identification and Its Response to Climate Change in Jilin,China

Jilin province is an important corn Production base in China. Due to climate change caused by meteorological disasters has a great influence on maize yield. The drought and rain waterlogging are common meteorological disasters during maize growth period. Using the temperature and precipitation data during maize growth period of Jilin province, we analyzed the trend of climate change. Further more, using the SPI index to identify the occurrence of drought and rain waterlogging, and by using probability density curve of SPI, we can characterize the risk of drought and rain waterlogging during maize growth period in Jilin. Further combining with the maize production data in Jilin province, we can get the analysis of the impact of climate change on maize yield.

Hazard Analysis of Drought and Flood Disaster for Maize in Xiliaohe Watershed

Based on the data of the climatic, maize growth and development, and the yield from 23 meteorological stations throughout the Xiliaohe watershed during 1961 and 2010. The whole growing period of maize was divided into three stages, early stage(from seedling to tasseling stage),medium stage(from tasseling to milk-ripe stage)and later stage(from milk-ripe to mature stage). According to water deficiency index to divide the level and hazard assessment model of drought and flood. In addition, spatial distribution of hazard in Xiliaohe watershed can be analyzed using the IDW method. Conclusions are as follows: (1)water deficiency index of maize in the early stage is on the rise but in the medium and later stages are on the decline. (2)The frequency above drought decrease from northwest to southeast in the whole stage, above medium flood increase from north to south in the early stage and later stage, and decrease from middle region to all around in the medium stage. (3)There are obvious regional differences about the hazard of drought and flood in Xiliaohe watershed.

Spatial-temporal Pattern of Water requirementin Xiliaohe Watershed

Based on the climatic data from 23 meteorological stations throughout the Xiliaohe watershed during1961 and 2010, every month’s spring maize water requirement are calculated by Penman-Monteith model, In addition, Spatial distribution of water requirement in Xiliaohe watershed can be analyzed using the Mann-Kendall and Ordinary Kriging method. Conclusions are as follows: Water requirement in Xiliaohe watershed is positive correlation relationship with T-max, mean wind speed and hours of sunshine. It’s negative correlated relationship with relative average humidity; As a whole, water requirement in the study area is on the decline. Every month’s spring maize water requirement first increased and then decreased. Water requirement is highest in July and is lowest in May; The spatial distributional trend of water requirement is that water requirement decreased from mid-east region to all around. Water requirement is larger,the crop take more risk.